As the speaker utters the exact reference sentence, if there were no ASR mistakes, the best strategy would be to consider the ASR output as the final translation (thus scoring 100% BLEU), simply discarding the MT output.

aSR is far from perfect, and we therefore experimented with several levels of aSR accuracy to study the combination of aSR and MT.


This approach yields translations for the majority of sentences.

for some of them (around 20% sentences for the considered dataset), the references still cannot be reached.


As we switch to "pessimistic" scheme, the number of "BAD" labels in the data increases which results in more partial matches.

the strict score does not follow this pattern.


Our experience in Quality Estimation led us to look at a novel approach based on sequences of adjacent words, socalled phrase, as a natural balance between the too fine grained word-and too coarse sentence-levels.

an intrinsic challenge comes along with this new level: how to find phrases which correspond to actual machine translation errors.


Empirically, it is shown that all words have real IDF scores that deviate from the expected value under a Poisson distribution.

keywords tend to have larger deviations than non-keywords.


In addition, GENIA consists of very short abstracts and as a result, many legitimate terms may be removed due to the frequency threshold and lexical pruning.

this can be easily rectified by relaxing the pre-filters.


For example, on the ACL RD-TEC dataset, phrases containing 'treebank' are very highly ranked.


This is clearly an invaluable resource for many computational applications.

for the specific purpose of our study, i.e.


the "window method" described in Seretan, 2011 for collocation extraction).

an initial extraction attempt showed that in a sentence window the two perceptual lexemes were seldom synaesthetically connected, as in (3): 3) Staffed by bright [SIGHT/Source] , young things who live and breathe music [HEARING/Target] , they tend to represent clients because they are passionate about their music.


The sentences that are extracted are therefore of the type in (4) and 5 Those in (4) and 5are good examples of synaesthesia, correctly identified in the corpus.

a rather heavy manual inspection of the extracted data was needed.


As for the types of sensory associations that have been found, almost every possible combination of senses has been attested.

in terms of frequency the directionality generalisation is confirmed: most transfers go from the lower to the higher modalities in both English (62%) and Italian (74%) (see Strik Lievers, 2015a for a discussion on directionality).


A quite heavy component of manual inspection of the extracted data is still needed.

if we take into account the extreme rarity of synaesthesia, then utility of the described methodology clearly emerges.


Most of the time, we would be tempted to simplify the model and treat all of them as multiword tokens or words-with-spaces (Sag et al., 2002).

accidental co-occurrence, like in example 2, creates ambiguities that are hard to solve at tokenisation time, specially given the simplicity of most automatic tokenisation approaches in French.


In previous experiments, we demonstrated that this approach is superior to treating all units systematically as words with spaces (Nasr et al., 2015).

this was only demonstrated for a small set of 8 CCONJs and 4 determiners in French.


2 There are already quite a few catalog entries of LRs that include MWEs in international infrastructures such as the CLARIN and META-SHARE repositories.

they are not always easy to find since the information about MWEs in these repositories is often scarce, non-uniform, or non-explicit.


(2007)), aims at estimating how familiar a term is.

the CHV only covers the English language, and limited attempts have been made to cover other languages such as French and Portuguese.


This is because, on the one hand, of the scarcity of annotated data with specialization degrees for French.

assessing specialization degrees manually is difficult to carry out and is a time-consuming task.


Collocation dictionaries, such as the Oxford Collocations Dictionary or the MacMillan Collocations Dictionary group collocations in terms of semantic categories to facilitate that language learners can easily retrieve the collocate that expresses the meaning they want to express.

this categorization (or classification) is not always homogeneous.


For instance, in the MacMillan Dictionary, the entries for admiration and affinity contain the categories 'have' and 'show', each with their own collocates, while for other headwords, such as, e.g., ability, collocates with the meaning 'have' and 'show' are grouped under the same category; in the entry for alarm, cause or express are not assigned to any category, while for other keywords the categories 'cause' and 'show' are used (see e.g., problem for 'cause' or admiration for 'show'); and so on.

in the case of some headwords, the categories are very fine-grained (cf., e.g., amount, which includes glosses like 'very large', 'too large', 'rather large', 'at the limit', etc.


Furthermore, thanks to the use of a precise annotation guide, it becomes possible to extract automatically information and apply machine learning techniques to study the distribution of different phenomena.

the type of information classically encoded into a treebank remains at a high level of generality, that moreover very often remain implicit.


It remains difficult to explore or compare the syntactic characteristics of various languages using the complete grammar (independently of the formalism, constituents or dependencies).

it is possible to compare some specific properties, in accordance with established practices in typology.


Weigthing the properties A first weight w 0 (equation 1) can be directly obtained by calculating the ratio of occurrences of the validating rules to the sum of both subsets -the properties satisfied in all cases corresponding then to w 0 = 1.

if w 0 allows a first filtering of the properties, it does not provide any information about their actual weight.


Most often, the creation of these out-of-domain treebanks follows the existing annotation guidelines created by the creators of the newspaper treebank, which would -at least in principle -make it possible to evaluate the generalization performance both of purelysupervised and domain-adapted parsers on these new datasets.

some caution is in order, as in many cases, annotation guidelines or even informal practices guiding the annotation deviate.In the case of Dredze et al.


We implemented different variations on the basic idea of keyness: from a simple ratio of the smoothed fre-quencies to Pearson's X 2 statistic, to an interval estimate of the odds (Johnson, 2001).

we found that a more intuitive way than comparing all rules or all rule bigrams found in the treebank was to compare just the rule bigrams for one particular category -Tiger has less than 25 categories of nonterminals, so it is definitely feasible to look at these individually -and to display these visually.


Of these, a small number (around 2-3% of the whole corpus) do not agree with existing annotations, and cursory inspection yielded a mixture of clear errors (a prepositional phrase with the NP node label), likely errors (an adverb that post-modifies a PP), and cases where only experts in the Tiger annotation scheme could make a firm prediction.

the modeling-based approach would find parses where an extraposed constituent was attached to a VP according to both parsers but to the S node in the treebank.


Many shallow natural language understanding tasks use dependency trees to extract relations between content words.

strict surface-structure dependency trees tend to follow the linguistic structure of sentences too closely and frequently fail to provide direct relations between content words.


Most systems that require a syntactic representation use basic SD trees which are guaranteed to be a strict surface syntax tree.

most systems that are concerned with the relations between content words use the collapsed or CCprocessed SD representations.


In Urdu, the case clitic ne is often indicative of an agent, and as 'ARG0' is mostly associated with agentivity, this can sometimes provide a clue about the identity of Arg0.

note that an Arg0 argument does not necessarily get 'ne-marking'.


However, having in mind the theoretical concepts of Czech functionally-oriented linguistics and general characteristics of the individual styles we tend to classify legal texts as texts belonging to the administrative-legal style (according to (Jelínek, 1996)) which is now earmarked as a unique functional style, standing next to other styles, such as professional, journalistic, literary or scientific.

due to their specific function legal texts in many ways overlap with the professional style.


For example, impersonal style of legal texts understandably excludes the use of question marks and exclamation marks.

we observe an extremely high usage of semicolon for purposes like enumeration, itemization and various types of listings.


Most published concept lists, however, only contain a concept label.

certain concept lists have been further expanded by adding structure, such as rankings, divisions, or relations.


This degree of diversity in concept labelling is important to check how well we have succeeded in linking the data, since wrongly assigned links will also yield diverse concept labels.

it reflects scholars' problems to denote concepts when compiling their concept lists.


For example, Thesaurus Rex categorizes the concept dog as both small and large depending on the context.

if our system chooses both attributes, we would have contradictory comparisons in the riddle.


Following the described process to generate the riddles, the subsequent evaluation points out that the word associations obtained by our system are useful for generating these riddles.

the evaluation also shows that a manual selection of comparisons is useful because confusing comparisons may be generated when the target of the riddles is a polysemic concept or presents some contradictory attributes.


At the beginning the sizes of plWN and PWN adjective domains were comparable 1 .

the process of mapping has been carried out parallel to the process of the extension of adjective category in plWN, and at the final stage of 1 This is based on the data from plWN 2.1 version, downloadable from http://nlp.pwr.wroc.pl/plwordnet/download/?lang=pl mapping the number of adjective synsets in plWN outgrew that of PWN twice 2 .


FrameNet (Baker et al., 1998) is such a resource and provides fine-grained semantic relations of predicates and their arguments.

frameNet does not provide an explicit link to real-world fact types.


to VN, which relies on a rich repertoire of more than 40 binary features to describe syntactic restrictions, MMO's descriptions of English frames make use only of 4 attributes: clausetype with 6 possible values, tense with 3 possible values, and the binary poss(essive) and num(ber The semantic restriction ontology.


As seen in previous section, the name variations found in infobox are in the structured form, making it easier to manually extract the correct name variations for any given topic.

it is well known that the goal of DBpedia (Mendes et al., 2012) is to extract structured information from Wikipedia and make it available on the web for querying.


The other candidate RDF schemas was Lemon (McCrae et al., 2012), the purpose of which is to enable people to be able to share lexicons on the Semantic Web.

to model a dictionary in Lemon, it is necessary to identify name variations by morphology, spelling variants, etc.


This is in part due to the lack of information about the licensing of the resources and ongoing discussions within the group about the use of non-commercial licenses.

we expect to reach a consensus within the next few months.


The life cycle typically ends with the distribution and publication of a language resource; this includes making a resource available by download or, for example, as Linked Open Data through an API.

the life cycle can continue if maintenance checks or user feedback result in minor updates or when a new version of an already existing resource needs to be prepared.


Allied to the growth in the amount of food data such as recipes available on the Internet is an increase in the number of studies on these data, such as recipe analysis and recipe search.

there are few publicly available resources for food research; those that do exist do not include a wide range of food data or any meal data (that is, likely combinations of recipes).


Others, like gensim (Řehůřek and Sojka, 2010), a topic modelling framework in Python, and Apache Lucene (Cutting et al., 2004), a Java library for document indexing and search, are designed for specific tasks.

projects GATE (Cunningham et al., 2011) and Apache UIMA (Apache, 2010), represent a comprehensive family of tools for text analytics.


For each operation in the pipeline, ESTNLTK comes with a sane default implementation.

a user can provide an alternative implementation through the constructor of the class Text.


In simple cases, it is sufficient to provide replacement components as keyword arguments to the Text constructor.

miscellaneous use cases may benefit from subclassing the Text class and develop the custom behaviour directly into it.


This can significantly speed up certain search operations, as linear scan over all documents can be replaced with simple index lookup.

the right structure of the index object depends on a particular task and is wasteful for online processing of documents.


This combined with the power of the Drupal-based Islandora (Islandora Community, 2016) for the online user interface provides a good starting point for FLAT.

to meet the very CLARIN specific requirements (i.e., support for CMDI and persistent identifiers) additional development work needs to done.


Some of this functionality could be provided out of the box by Islandora, e.g., file type checking using the FITS (OpenScholar, 2016) solution packs.

islandora ingests new objects directly into the Fedora Commons repository, while in FLAT a temporary workspace is required where further ingest is halted until collection managers have reviewed the contents.


It will therefore enable the investigation of existing research questions in new ways, create opportunities for investigating research questions that could not be addressed before, and for formulating and investigating completely new research questions.

this digital turn is not going to be easy!


On the one hand, it implies vast efforts invested in persuading IPR holders to contribute to a cultural action in a way that does not hinder their marketing plans.

it means reaching agreement on what texts and how much of them to include in the corpus.


In the training data, however, only a limited number of lemmas appear frequently enough for a reliable language modelling, and many words in new texts are out-ofvocabulary.

there are many categories of words (such as numerals or several groups of proper names) with identical syntactic behaviour.


Besides, in both cases, a web interface is offered for easier access to corresponding tools, and, moreover, for using tool chains for solving specific text analysis tasks.

it must be noted, that, while CLARIN-D already has a mature linguistic chaining tool WebLicht 7 (Hinrichs, Hinrichs and Zastrow 2010) with the web interface access, the web interface and tool chaining service for LKSSAIS is less flexible and still in its development phase, lacking convenient configuration management and visualization options.


For this reason presently LKSSAIS provides only GUI and API based services for developers and CLARIN ERIC community.

it is expected that the Lithuanian legal framework will change in the near future.


The fact that the harmonised vowel is always front and unrounded is presumably related to a pronounced-but unwritten-epenthetic vowel that occurs between ‹б› and ‹ль› in the bare stem forms.

since no vowel is inserted in forms with a following vowel (e.g., ансамбли, рубли), this phenomenon provides an interesting case of phonological opacity-an analysis of which is beyond the scope of the present paper.


zāle: [zãle] (level tone) 'hall, large room' vs. [zâle] (broken tone) 'grass, herb'.

two specific graphemes -'e' pronounced as 'e' or 'ae', and 'o' pronounced as 'uo < ' (as in doma 'thought'), 'O' or 'O:' -require an informed choice to pronounce the word correctly, and the pronunciation may vary across inflectional forms, even with the same spelling.


Estimated number of native speakers are approximately 59 million in these two countries (Khubchandani, 2003).

it is also spoken by people in various other countries.


In the first paradigm, called avenir we can see that x 1 always ends in the letter v, and that x 2 is always the string n. 2 In the second paradigm (negar), there is no clear pattern regarding the shape of x 1 .

x 2 in all 14 inflection tables that produced the paradigm, is always the string eg.


For example, only one lexeme is considered since both its meanings ('vapour' and 'pair') have exactly the same inflectional forms.

sGJP has three lexemes with the lemma -, since its 3 meanings ('swimmer', 'great diving beetle', and 'float') result in paradigms differing in the accusative.


This situation is strictly related to the still quite limited amount of linguistically annotated textual data for Latin, which can help the building of new lexical resources by supporting them with empirical evidence.

projects for creating new language resources for Latin have been launched over the last decade to fill this gap.


projects for creating dependency treebanks for Latin have been launched over the last decade, as well as for creating fundamental lexical resources, like the (still very small) Latin WordNet (Minozzi, 2010).


Like these well-known projects, our primary research goal is to establish a computational method to discover crosslingual correspondences in a word-sense/lexical-concept level, given LSRs in different languages.

we further explore a methodology for classifying discovered cross-lingual correspondences with a broader range of semantic relation types, not limited to synonymy.


That is, the two-tiered classifiers configuration could be more feasible for classifying cross-lingual correspondence candidates be-  tween different LSRs.

further experimentations with a larger data set would be necessary to make this more concrete.


For instance, an Instrument for PISAĆ 'write' could be a pen, a ballpen, a pencil etc.

in PLWORDNET their direct hypernym is artykuł papierniczy-1 'writing materials' which is evidently too wide (as it includes, e.g., 'notebook').


That is, they lexicalize the receiver of the action (the Buyer) as the complement of a PP headed by {a}.

(9) Ho venduto il libro a Giulia ( I sold the book to Julia) COMPRARE verbs reflect the Buyer's perspective, which in this case is the Agent that acquires some Goods from a Seller.


Verb classes have been defined as sets of semantically related verbs that share the same patterns and constructions.

the differences between the two systems notwithstanding, the resulting classification remains compatible with VerbNet/Levin taxonomy.


(2012) performs offline clustering of word contexts and thus is computationally expensive for large corpora.

adaGram can be considered as an online clustering of contexts, which therefore can scale to large corpora keeping a reasonable memory footprint.


The aim of this project is to link together different predicate resources via manual mappings.

the Predicate Matrix is built by automatic methods.


Then, for each lexical unit, SemLink also supplies a mapping between the semantic roles of PropBank and VerbNet, as well as the roles of VerbNet and FrameNet.


For example, the ili ili-30-00007739-v represents the English synset eng-30-00007739-v blink 1 wink 3 nictitate 1 nictate 1 and the Spanish synset spa-00007739-v pestañear 1.

in the new multilingual Predicate Matrix this Spanish synset also have associated the Spanish word senses parpadear and guiñar.


One of the few projects working on the integration of the predicate information is SemLink (Palmer, 2009).

the mappings of this resource has been developed by manual means and only cover verbal predicates.


Princeton WordNet identifies synonymy, antonymy, troponymy, hypernymy, entailment, and cause.

verbOcean identifies similarity, strength, antonymy, enabled, and happens-before.


At the same time, it is a rather broad relation, hence it can be acquired with a good precision (0.56).

intensity occurs rarely, and is also a more strict relation.


Overall, VCO-Pre has a macro-precision of 0.40 and a rather low macro-recall of 0.10.

vCO-Cov has well-balanced macroprecision and macro-recall of around 0.37.


(2008) present an approach which reduces sample selection bias by first exposing it in the dataset structure through clustering, and then rebalancing the dataset.

this method requires the availability of additional unlabeled data.


For polarity classification, COP leads to a significant better result, which reflects its sentiment-oriented nature.

combining other features with COP still leads to significant improvement, indicating that adding semantic information helps for polarity classification.


Among the other features, WV is still the most informative feature.

it does not dominate SSI, indicating the possibility for fine tuning the word embedding with prior knowledge (SSI in our case) as in (Faruqui et al., 2014).


Some of the recent systems that have emerged are (Toh and Wang, 2014;Chernyshevich, 2014;Wagner et al., 2014;Castellucci et al., 2014;Gupta et al., 2015).

almost all these research are related to some specific languages, especially the English.


Several benchmark datasets for sentiment analysis for resource-rich languages like English exist and these have been made freely available for research, e.g., SemEval 2014 datasets (Pontiki et al., 2014).

indian languages are still far behind in terms of such resources.


The first review contains only one aspect term and its polarity is positive.

the second review does not have any aspect term.


This is what is known as Standard Modern Arabic (MSA) which may be seen as a simplified version of Classical Arabic 2 (CA).

when it comes to daily life, MSA is rarely, if at all, used as many people find it ridiculous to use MSA with their friends or families, instead they use their own dialects.


Words which can be found in an MSA dictionary: this means that these words do exist in MSA.

many of them have a conflicting part of speech (PoS) or totally a different meaning, i.e.


In example 3, the word العافية [wellness] is used in Gulf Arabic in the same meaning as in MSA which is positive.

in other dialects, namely in North African, العافية means fire which is negative.


For instance, if a document contains more than one sentiment of the same polarity (positive/negative), the document, whatever its length, is counted as one (positive/negative) document.

if it contains any number of different opinions given that there is at least one positive and one negative, the document is classified as mixed.


Of course, there is a better alternative to the non-match, namely apply a preprocessing step (normalization and spelling correction) to the corpus.

this is not very helpful in our case because of the different usage of words between MSA and Gulf Arabic and more important diacritics are ignored.


These approaches can capture some aspects of the shifters effectively.

they depend upon the availability of an annotated corpus in which shifter words and their scopes are tagged.


There are several theories of discourse structure for texts: RST (Mann and Thompson, 1987), LDM (Polanyi et al., 2004), the graphbank model (Wolf and Gibson, 2005), DLTAG (Forbes et al., 2003), PDTB (Prasad et al., 2008), and SDRT (Asher and Lascarides, 2003).

data from our corpus rule out DLTAG, LDM, and RST as candidate theories because they posit tree-based discourse structures.


Other frequent relations are: ELABORATION, EX-PLANATION, CONTINUATION, PARALLEL, CONTRAST, ALTERNATION, and CONDITIONAL, variants of which are also used in many models for the discourse annotation of single-authored text besides those based on SDRT.

other relations, in particular temporal relations like NARRATION, TEMPORAL-LOCATION or BACK-GROUND, are not at all frequent in the Settlers corpus.


Originally every game, each one of which contains between 100 and 900 dialogue turns, was to be split into negotiation dialogues (up to several dozen) in which one person is in charge of the bargaining after a roll of the dice.

we noticed that, often, even if the person in charge of bargaining changed, a conversation that had begun during a previous bargaining episode, continued into the next episode.


Our approach belongs to the category of example-based dialogue modelling which aims at using a database of semantically indexed dialogue examples to manage dialogue (Lee et al., 2009).

our purpose is a complete automation of this process from the creation of the database to the conversational management process to avoid the need of a costly and time-consuming human intervention.


As of the repetition of the system (see figure 3), participants reported that the system has been "2: repeating itself" (mode=2; median=3), where 1 is "repeating itself a lot" and 5 is "not repeating itself at all".


We have not found significative links between reported enjoyment of the interaction and dialogue features or system capabilities (such as dialogue length and coherence).

we have found a significant correlation with the reported mental state "enthusiasm".


Dialogue characterization can be achieved by modifying the utterances created by generic dialogue systems using a characterization module.

a trade-off occurs between the significance of characters in dialogue systems and the quality of modified utterances.


The resource of movie scripts, such as IMSDb, is good enough to generate conversational discourse for dialogue processing.

monolingual movie scripts are not enough for MT which needs a large-scale bilingual dialogue corpus to train and tune translation models.


The reason is that neither of the reference-based evaluation systems explicitly addresses this aspect of translation quality.

bLEU and Meteor are outperformed by UPF-Cobalt in terms of the correlation with fluency judgments.


Phrase-based statistical machine translation (PBSMT) is considered as state-of-the-art MT approach whenever sufficiently large parallel (or comparable) datasets for training are available.

for many language pairs and translation directions (English to Portuguese among them) large training datasets only exists for few domains, such as parliamentary discussions (Europarl (Koehn, 2005)) or legal documents (JRC-Acquis corpus (Steinberger et al., 2006)).


In this regard, our own previous work demonstrated a potential step in the right direction by suggesting that incorporating the output from running WSD as contextual features in a maxent-based transfer model results in a slight improvement in the quality of machine translation (Neale et al., 2015).

to two other approaches we had experimented with -'projecting' word senses into source language input sentences prior to translation either by completely replacing source language lemmas with synset identifiers or appending those synset identifiers onto the source language lemmas -we showed that the reported gains were possible without having to reformulate the word senses themselves nor the algorithms used to retrieve them, as most of the successful marriages of WSD and machine translation reported in the literature had resorted to.


The user ran a series of scripts on that data.

superCAT is used via a single Java .jar file.


One would thus expect that proper names fall into the major class neither count nor mass, which is correct for the majority of proper names identified in BECL.

bECL also contains proper names that are members of the other classes, as illustrated in (7).


As mentioned above, kind-of -readings cannot be in all cases easily distinguished from other shifting types.

roughly 25% of the final examples showing mass-to-count shifting clearly allow this interpretation.


Most of them use a bag of words model to compute sentence similarity.

the bag of words model is sometimes inadequate for capturing the syntactic and semantic similarities among the sentences, which may affect the qualities of the summaries.


To model the importance of a word, we include the tf-idf (term frequency -inverse document frequency) values of the dependent and head words to the formula and form the TF-IDF Based Approximate Bigram Kernel (TABK) as defined below: where N (A) is the normalizer function: and sim t is defined as: TABK does not encourage consecutive bigram matches that form a subtree in the dependency trees.

a common subtree in the dependency trees of two sentences means that these sentences contain similar substructures.


Similar performance with the state-of-the-art submodular functions approach is achieved.

the improvement in the performance of LexRank by our typed dependency tree kernels is not found to be statistically significant.


Specifically, we look at the verbparticle (V-Prt) split in English and German, such as in the examples below: (1) take V the shoes off P rt (2) macht V schon wieder blau P rt From a syntactic perspective, these two languages behave very differently: in German, the particle position is clausefinal and a particle can be separated from its verb by an embedded clause, while in English a particle can be separated from the verb only by its direct object and a very long split is impossible.

from a MT perspective, these constructions are rather similar as they involve the same type of lexical items (verbs and particles) which must be translated in connection to each other for a correct output.


Approximately two-thirds of all verbs are aligned well.

looking closely only at the 59 finite (split) V-Prt phrases present in the test suite, we can note that only 22 are properly aligned with their English translation.


We evaluated the English test suite as translated by a phrase-based system.

since there were no readily available syntax-based systems for the translation between English and French, we instead show a comparison with Google Translate 2 .


In cases such as 'take (the shoes) off', mentioned above or 'make (these things) up', the literal translation does not make sense because the verb phrase meaning is non-compositional.

in cases such as 'bring in' the meaning of the simple verb 'bring' is very similar to the meaning of the bigger phrase and the literal verb-only translation is acceptable.


When an author uses the weak word long in a context like long time period, readers differ about what long in this context actually means.

not every use of a weak word lemma is necessarily imprecise or ambiguous.


The sentences in C-WEP only contain a small number of typos, most errors are "grammatical errors."

both checkers mark a high number of "spelling issues," which are listed in table 1.


The versions could be ranked by edit distance measured in words; ranking could also take into account the position of editing operationse.g., edits later in the sentence would result in a higher rank.

an even better way would be to prevent errors in the first place by supporting the author during editing.


As for identifying the error type, there are too many words including the duplicate nicht, and the error involves both the POS verb and preposition.

depending on the target hypothesis, the error is only in the verb or only in the preposition.


When the cooccurrence-based metric is used we observe that the model lacks robustness, and performance drops especially for large number of seeds.

when RR is used performance is robust and also the correlation curve is quite close to the one observed when context-based similarities are used.


The so called sentence-level sentiment classification determines whether each sentence expresses a positive or negative opinion, or it is neutral from the polarity point of view.

we should note that many sentences can imply more than one opinion on more than one target.


Also, we supposed at the beginning of the annotation that words that express negation can mostly be covered by negation words like nem or sem 'not', the negative forms of the copula like nincs or sincs 'is not' and some postpositions like nélkül 'without'.

it turned out that negation is expressed by a wider variety of words and phrases than expected, for instance: elillan 'disappear' nélkülöz 'miss' bizarr lenne aztállítani 'it would be strange to say' helyett 'instead' semmi köze sincs 'it has nothing to do with' nulla 'zero' lespórol 'spare' Altogether, there are 3516 negation words in the corpus, including 2587 adverbs, 468 verbs, 145 pronouns and 93 conjunctions.


On the basis of the results we concluded that the two types of intensifiers together occur with the same frequency in positive (6693:2706) and negative (8053:3347) fragments, for instance, nagyon jó 'very good' and nagyon rossz 'very bad'.

frequency of intensifiers with decreasing semantic content is not the same in the two types of sentiment fragments: they occur much more often in negative polarity fragments (8053:779) than in positive ones (6693:301).


So far, sentiment analysis has mainly focused on the detection of explicit opinions.

recently the relevance of implicit opinions has received broader attention within the field.


There may be contexts in which the theme's creation is more salient, for example, when burning your initials into a tree.

the correct placing of the theme in the target location may be more prominent in situations when considering whether bedrock is suitable for driving a tunnel into.


At this time, we limit ourselves to annotating GermaNet synsets relative to what is provided in the syntactic valence frames that come with the example sentences.

we still want to distinguish between predicates that are strictly stative and therefore do not involve a causally responsible agent and predicates that do allow a causal agent but which may not be realized in the provided valence frames.


We could thus in theory collapse all functors sharing the same structure into a single more abstract functor.

we refrain from doing so because the functor labels convey additional information that is useful for other purposes.


The second most common response was neutral evaluation, which may seem somewhat surprising if causal forces are usually evaluated in the same way as the resulting state.

recall that what functors provide are defeasible implicatures rather than firm entailments.


That is, the main clause (predicate) is non-factual as well as the subclause (predicate).

in The minister forces the president to cheat both are factual.


How Description Logics can be used in order to identify so-called polarity conflicts is described in Klenner (2015).

the relations between the referents and again, the factuality of situations are not part of this model.


The impression is that, on the one hand, the participation on Twitter has been more critical, free from the constraints of the debate and, especially, individual.

the web platform provided for the online consultation contributions collected from groups of individuals, which collectively took the responsibility to synthesize and publish the contents.


Indeed, among 5,573 tweets in agreement by topic, the 80% were labeled with the generic topic tag.

the distribution of topics in the two corpora, TW-BS and WEB-BS is different, as can be observed in Figure 4.


And many useful and high-quality semantic resources have been built, such as FrameNet (Baker et al., 1998), PropBank (Palmer et al., 2005), Academia Sinica Treebank (Huang et al., 2000), NomBank (Meyers et al., 2004) , VerbNet (Schuler, 2005), HowNet (Dong and Dong, 2003), etc.

each of them only serves its own purpose.


However it has not been widely used in SMT tasks.

propBank has been widely used in both SRL and SMT since CoNLL-2005 (Carreras and Màrquez, 2005).


Intuitively, because the NN features give a measure of the bilingual similarity of a sentence pair, they could be helpful for this task.

this assumption has not been verified previously.


Most of the increased classif ied well sentence pairs (4733→4869) belong to this type, which significantly improved the recall.

low NN feature values also could be harmful.


(Dabre et al., 2015) used the NN features for a pivot-based SMT system for dictionary construction.

we score the sentence pairs of with a neural translation model, and use the scores as NN features for parallel sentence extraction from comparable corpora.


PubMed is the largest database for scientific publications in biomedicine, and has been extensively used in many biomedical natural language processing applications.

only titles are available in more than one language in PubMed (Wu et al., 2011).


This is certainly a good feature of our corpus, given that previous parallel corpora of biomedical publication were restricted to MEDLINE titles (Kors et al., 2015;Jimeno Yepes et al., 2013).

the monolingual datasets are mainly composed of titles, due to the same reason stated above, i.e., the existence of many articles whose titles were been translated to other languages.


experiments provide a positive evaluation of the corpus quality as a whole.

in our manual review of sentence alignment, we found some examples where the language quality of the corpus was lacking.


Many sources of bitexts have been identified; some examples are: • texts from multilingual institutions, such as the Hansards corpus (Roukos et al., 1995) or the Europarl corpus (Koehn, 2005); • translations of software interfaces and documentation, such as KDE4 and OpenOffice (Tiedemann, 2009); or • news translated into different languages, such as the SETimes corpus (Ljubešić, 2009), or the News Commentaries corpus (Bojar et al., 2013).

one of the most obvious sources for collecting parallel data is the Internet.


In the harder translation direction, English→Croatian, the newly built SMT systems outperform two of the reference systems, Bing and Yandex, while we do not observe a substantial decrease in the quality of the MT system built solely on the hrenWaC parallel corpus compared to the system built on all the training corpora.

in the opposite direction, Croatian→English, there is a significant difference in the performance achieved by both newly built MT systems: the system using all the training data achieves a 2.22 BLEU points increase and a 2.12 TER points decrease when compared to that trained only on the hrenWaC corpus.


As can be seen in these results, the SMT systems obtained are not able to outperform all the third-party MT systems used for evaluation.

it is worth mentioning that, given that the data used for building these models was obtained in a fully automatic fashion by crawling TLDs, the results are quite positive, since they show that it is possible to obtain an MT system comparable to some of the most used online MT systems by only running an automatic crawling process for a few days, with the only explicit input being a TLD to be crawled and a small bilingual English-Croatian lexicon.


For example, people seldom engage with surveys that require them to report their height and weight.

such data is crucial for training automated public health tools, such as algorithms that detect risk for (preventable) type 2 diabetes mellitus (T2DM, henceforth diabetes).


In all, we generated 33 questions that cover all decision nodes in the random forest classifier.

when taking the quiz, each individual participant answered between 12 and 24 questions, depending on their answers and the corresponding traversal of the decision trees.


Overall we find a strong correlation of 0.82; the correlation remains strong, 0.79, if tweets are counted a half hour before broadcast time.

the two most popular TV shows account for most of the positive effect; if we leave out the single and second most popular TV shows, the correlation drops to being moderate to weak.


These results can be interpreted as implying that estimated TV ratings could already be publicized at the start of the show.

the high correlation drops to medium or low correlation when the single or two most watched shows are left out.


A popular approach used by recommendation systems is collaborative filtering for identifying users similar to a target user based on their purchase history, and then recommending products that similar users have already bought but the target user has not (Schafer et al., 1999;Sarwar et al., 2000).

in this approach, it is required that a target user has bought something before.


(2009) implemented a content-based movie recommender system capable of "cold start" by using preference tags that customers labeled movies within in a movie review service.

to these works, our goal is to infer the future purchase behavior of a customer who is interested in a product.


In Table 5 we observe that only a small percentage of users who indicate that they want a product by tweeting one of the phrases in Table 1 actually bought a product.

we note that many of the users expressing a buy phrase did buy a product.


(2015) reported their results using cross-validation on the Test-Stanford data set.

we used the Test-Stanford data set as a test set only and tuned our parameters using our manually created development set (Dev-Stanford).


This variation is one of the driving factors behind language change.

investigating language variation is a complex undertaking: the more factors we want to consider, the more data we need.


Traditional, qualitative methods are often not designed to handle more than a handful of data points (albeit in depth).

computational, quantitative methods offer the possibility to explore language variation at an unprecedented scale.


The most wellknown one is certainly the Google Ngram corpus (Michel et al., 2011), which enables lexical search over enormous amounts of text.

it does not include demographic factors, only time of publication, and has recently been criticized for inherent biases (Pechenick et al., 2015).


Tweet analysis has led to a large number of studies in many domains such as ideology prediction in Information Sciences (Djemili et al., 2014), spam detection in Security (Yamasaki, 2011), dialog analysis in Linguistics (Boyd et al., 2010), and natural disaster anticipation in Emergency (Gelernter and Mushegian, 2011;Sakaki et al., 2013).

complementary efforts have been made in Social Sciences and Digital Humanities to develop tweet classifications (Dann, 2010;Riemer and Richter, 2010;Shiri and Rathi, 2013;Stvilia and Gibradze, 2014 few studies aim at classifying tweets according to communication classes.


In the former, words occurrences are assigned sense labels from a predefined sense inventory.

sense discrimination (Schütze, 1998) addresses a simpler task of differentiating among the different uses of a word, without a reference to a sense inventory.


Context clustering exploits the distributional hypothesis (Harris, 1954) to group together the similar usages of a word.

word clustering groups together different but semantically similar words that pertain to a specific sense (e.g., money, loan, and finance for the financial sense of bank).


The shape of a co-occurrence graph crucially depends on the size of the corpus: if the corpus is too small, many relations will not be present, and rare senses will not be captured.

a large corpus may yield a noisy co-occurrence graph.


1 The subgraph consists of all first-and second-degree neighbors of the target word.

to keep the size of the subgraph manageable, we increment the edge weight threshold as we move away from the target word.


We described an intrinsic evaluation setup, in which Chinese Whispers algorithm outperformed Markov Clustering.

in word sense disambiguation (WSD) evaluation, Markov Clustering emerged as the winner, with a rather good accuracy of about 75%.


(2012), and Henrich and Hinrichs (2014).

none of these provide semantic role annotation.


The VerbNet role hierarchy follows two main principles: lower-level roles are more specific, and restricted by semantic properties and constraints; consequently, roles in a parent-child relation tend to not co-occur.

the hierarchy contains multiple inheritance links and is therefore difficult to conceptualize (cf.


The changes we propose for the original VerbNet role inventory in Figure 1 are kept as small as possible.

our aim is to strengthen the semantic principles that underlie the role hierarchy in a more systematic way.


The VerbNet hierarchy was broken down to a flat list of roles, following the model of SemAF-SR. Further, we merged some of the roles to 'multi-roles' on the assumption that the reduced and coarser role inventory makes it easier to distinguish roles.

this assumption was not confirmed, as we obtained low IAA for RI-I.


The frequent role sets shown in Table 6 typically occur with many predicate types.

we also observe that alternating role sets can be assigned to a specific predicate sense, as illustrated in Table 7.


It was seen that most of the time the literal senses of a word were placed in ranks above the metaphorical or figurative uses.

at times the ranking order did not adhere to the above mentioned criterion.


Automatic subtitling was born in response to a high subtitling demand, as a more productive alternative that enabled subtitling in challenging situations, such as live broadcasts, where traditional subtitling was not directly applicable.

at present, automatic subtitling is yet not capable of creating subtitles that equal human quality and, thus, its focus is on facilitating the generation or post-editing of automatic subtitles by professional subtitlers, both in live and pre-recorded settings.


Its precision reaches 97-98%, the generated delay is low and the severity of errors medium.

learning this technique requires a long time (around three years) and the cost is high (Romero-Fresco 2011).


The latter tech-Listing 1: JSON Metadata Format nique is relevant for the exploration of photo archives based on image caption text.

in order to develop and deploy these techniques, a large dataset is needed.


The image datasets provide textual descriptions written by multiple human annotators per image, and are often used during evaluation as gold-standard reference descriptions against a system generated candidate description.

little work has been done to analyse or evaluate the gold-standard descriptions against themselves, i.e.


As mentioned, noisy, largescale datasets with user-generated captions exist for news images (Berg et al., 2004;Feng and Lapata, 2008) and Flickr (Ordonez et al., 2011;Chen et al., 2015;Thomee et al., 2015).

in this paper, we are mainly interested in literal descriptions of what is depicted in the image, rather than non-literal or non-visual descriptions that require significant inference from additional knowledge about the image context.


ROUGE-W1.2's absolute scores are lower than ROUGE-L as the measure penalises non-contiguous common subsequences.

to BLEU, ROUGE is not sensitive to number of descriptions per image, as it performs averaging over all reference descriptions.


In ASL, hand, arm, upper body, and head movement conveys important linguistic information of various kinds, as do facial expressions.

since moving body parts are more relevant for recognition than stationary ones, we extract relevant features solely from image regions with significant motion, without restricting attention to specific body parts.


The other most influential taxonomy is proposed by Spiegel-Rösing (1977), with 13 categories.

80% of the citation purposes could be classified in one category: Cited source substantiates a statement of assumption, or points to further information.


The lead-based baseline outperforms all the methods from the literature that we included.

our framework outperforms this baseline.


Based on the manual evaluation results (Tables 3, 4 and 5), our framework outperforms the commercial system for the Popular and Random Partner datasets.

the same trend was not reflected for the Random Other dataset.


the issue of which was the best film of 2015?.

issues are rarely explicitly articulated in reader comments, (or the news article, see e.g.


If the model is not consistent, it will be penalized.

the penalty must be applied in such a way as to give it maximal benefit of the doubt.


Prefixed words cannot be called compounds in the strict sense of the term because prefixes are not independent lexical units.

some prefixes are very close to the neoclassical roots, compare prefix biwith neoclassical root uniaccording to (Béchade, 1992).


The UniMorph Schema includes the features necessary to distinguish all these categories, which are marked by surface contrasts in each language and are not decomposed further in any natural language.

5 were a language to be discovered that distinguished a blended dual-trial (2/3) from singular, paucal, greater paucal, and plural, the UniMorph Schema would combine the minimal dual (exactly 2) and trial (exactly 3) features together additively to annotate the blended category (as DU+TRI).


The average number of inflected forms collected per paradigm differs across systems, as Liebeck and Conrad (2015) only considered forms which fit certain Wiktionary templates and Durrett and DeNero (2013) extracted only paradigms for which they could obtain a fixed set of forms (their software requires all training paradigms to be equal in size).

our system extracts all paradigms, regardless of completeness.


The intention sometimes can be fulfilled in one single domain (i.e., an app).

it is possible to span multiple domains and requires information coordination among these domains.


In other word, user can mentally create his own virtual app on top of existing ones.

although intelligent agents can be configured by developers to passively support (limited) types of cross-domain interactions, they are not capable of actively managing apps to satisfy a user's potentially complex intentions, because they do not consider the repeated execution of activities in pursuit of user intentions.


By contrast, spoken language can effectively convey the user's high-level and complex intentions to a device (e.g., Apple Siri, Amazon Alexa, Google Now and Microsoft Cortana).

speech presents the challenges about 1) understanding both at the level of individual apps and at the level of tasks that span apps; and 2) communicating a task-level functionality between user and agent.


We were informed by participants that they made use of this feature.

we did not solicit further information about frequency of use or categories of events.


As shown in Figure 3, SETTINGS would deal with U 1 to setup bluetooth connection and MUSIC would take care of U 2 and U 3 .

sometimes users produce utterances which may involve several apps, e.g., "Boost my phone so I can play [game] spiderman" requires CLEANMASTER to clear the RAM and the game SPIDERMAN.


For example, the annotation of the following utterance could depend on the context of the dialogue: OK, let's move to pension fund If this utterance is a reply to some Offer, then it would most likely be annotated as Accept, referring to the previous Offer, along with Query(Offer=Pension Fund).

if vaskonov/negochat_guidelines/blob/master/ guidelines.pdf 8 Two labels are considered to be in agreement only if all of their corresponding components are identical.


As we discussed in the metrics section, it is difficult to interpret the performance of these detectors from the numeric values.

it is interesting to note that the best performing team in the classification-related metrics did not perform as well in these metrics.


For example, pression artérielleélevée ('high blood pressure', input) is mapped to hypertension (clinical record) thanks to their common CUI (C0020538).


In developing our guidelines, we took the earlier MSRP alignment guidelines into account where they were consistent with the Edinburgh ones.

contrary to the MSRP guidelines-where annotators were not told whether two sentences were supposed to be in an entailment relationship-we considered it essential for the alignment decisions to be consistent with the overall decision as to whether the two questions were taken to be paraphrases in the dialogue context.


On the one hand, given the objective of the study, a fine grained study of the form -function relationship requires a fine grained functional analysis, too.

a part of the literature clearly gave up annotating levels of communication in feedback, as for example Bunt et al.


In the graph we can see that there were a number of occurrences of pastel red (correctly written short vowel markation) so it seems most children have mastered this skill in the post test.

we can also see that about three kids deserver further study.


be.1sg in.the beach 'I am in the beach'.

more omissions with adjectival predicates than with nominal predicates were observable, as opposed to what happened at the elementary level: (9) A praia Ø fatástica, ondas Ø boas e pessoas Ø alegres e simpáticas.


Chinese students are clearly the ones producing more errors.

when compared to the English students, we see that the increase in errors is not proportional to the increase in number of words in the corpus.


Performing the chi-square test on the null hypothesis that the number of spelling errors with a Damerau-Levenshtein distance up to 2 and over two, and the clinical status of a subject are independent, we receive a p-value of 0.8783, because of which we can not reject the null hypothesis.

there is a visible difference between the percentage of spelling errors of distance 1 and 2.


One has, of course, bear in mind that among subjects with language disorders, these errors will still occur five times more frequently than among healthy participants, making their texts after spelling correction still less accurate.

there is statistically significant difference in the number of spelling errors of distance 1 and of distance 2 between the two groups.


On the other hand, there is statistically significant difference in the number of spelling errors of distance 1 and of distance 2 between the two groups.

both types of errors can be dealt with by using traditional spelling correction approaches.


As the number of candidates increases, both evaluation metrics should increase as well.

one should expect a negative impact of too many candidates being shown to the user, especially among participants with language disorders for whom reading is an issue.


Our research has pointed us in the direction of Mediaeval being the original print font ( De Drukkerij E. J. Brill, 1932), with a digital representation being found in Dutch Mediaeval; the latter, however, appears to be difficult to obtain, and also differs from the original font in the shape of its serifs and that of various capital letters.

even if we had been able to retrieve the original font, text printed on a 100-year-old typesetting machine in comparison to that generated by modern typesetting software might differ too strongly in appearance to train a language model for the textual source (Nagy et al., 2000).


Three aspects that make this toolkit suite stand out, and that -to our knowledge -no other tool or toolkit can provide, are that (1) it presents confusion matrices and accuracy values for single characters and words, (2) it comes with an extensive set of separate tools that each assess and highlight different performance metrics, and (3) it is the only toolkit suite in existence to have been used as a de facto standardized assessment tool.

non-ASCII text was causing troubles to this toolkit from the mid-nineties, when OCR operations were still focused on purely ASCII texts (Beusekom et al., 2008).


Secondary data usually works as prime data in publication.

in a language study, primary data itself is prime data in publication.


So far, it has been taken for granted that software of multimedia players is used to ensure the relationship.

in order to make sound data as primary data in linguistics, sound data should ideally be independent from any application environments.


External researchers may discover other novel uses for the corpus, in both corrected and uncorrected forms.

the need to develop a robust method for Welsh-language text anonymization prior to more widespread distribution is a significant hurdle that must be overcome.


In Figure 7(a), even though the non-pivot words are connected by four pivot words representing four senses/meanings, the transgraph only has one translation pair candidate (w A 1 -w C 1 ) and so the precision is 100%.

polysemy in pivot language negatively impact the precision.


So the part of the corpus to be published avoids such content.

the remaining parts still contains lots of references to third persons.


It did not provide additional name reference candidates to the other approaches.

a match with the list of concepts might help to decide whether or not a name should be anonymized because the list contains mainly well-known persons or places.


In the recent years, named entity recognition systems gained great popularity on the Web.

these systems, at large, do not evaluate the actual importance of the recognized entities in the documents.


tagging 'Yellow Submarine' as dbpedia.org/page/Yellow Submarine (song).

this is not a trivial task as mentions to Music entities show language and register idiosyncrasies (Tata and Di Eugenio, 2010;Gruhl et al., 2009), and therefore a certain degree of tailoring is required in order to account for them.


Arabic script does not, when writing Arabic, represent short vowels or make a distinction between long high vowels and glide consonants.

this can be a source of greater ambiguity when Arabic scripts are used to write languages in which vowels carry a higher functional load -in particular, Indo  We implemented and trained a character level linear chain conditional random field (CRF) based system for converting the Perso-Arabic script for Sorani to IPA.


The features are technically three-valued: plus (+), minus (−) and unspecified (0).

unspecification is used sparingly in the database and 0 values can be safely recoded as + values.


This suggests that active contribution to guideline development is more useful for subsequent annotation than simply following the guidelines developed by others.

to the pilot phase, the main phase showed clearer trends.


The former NER output is clearly preferable to the latter; because, in the former case, lung and cancer are semantically related to the reference annotation; the meaning of the the top level entity (lung cancer) can be composed from the annotated entities.

only recognizing cancer should arguably be penalized, since it is not as informative.


Knowledge Base Population (KBP) (Ji et al., 2014), the process through which the entities identified by NEL systems are used to populate new knowledge bases, is another useful technique for exploring the relations between entities.

since NEL or KBP evaluation tasks might require a new corpus or at least a new gold standard, and the creation of such resources requires significant effort, there is a desire to automate steps in the corpus creation process.


By exploiting the knowledge graph built with named entities from a corpus, new gold standards can be created for specific tasks like KBP, entity contextualization or enrichment.

to research focused on social media (Bontcheva and Rout, 2012), we used regional news and analyzed transcripts from the German regional broadcaster RBB (Rundfunk Berlin Brandenburg).


Besides the Wikipedia web site, Wikipedia data is available via a RESTful API as well as complete XML dumps.

aPI access is officially limited to one request per second, prohibiting a web scraping approach to acquire the data.


During annotation, we encountered cases, in which two patterns contradicted each other, e.g., "person|SPOUSE person|SPOUSE divorced in date|TODATE" ⊥ "person|SPOUSE was married to per-son|SPOUSE until death in date|TODATE.

these cases were rare and we did not annotate them separately, as the entailment graphs we construct only capture binary decisions (entailment, non-entailment).


To summarise, the algorithms used in the EOP have not succeeded in coming close to getting enough the correct answers to pass the USA national bar exam.

they can reliably identify the wrong answers.


The RTE datasets are widely applicable for number of applications, such as Question Answering, Information Retrieval or Information Extraction.

the STS/SR task requires to identify the degree of similarity or relatedness that exists between two text fragments (phrases, sentences, paragraphs, etc), where similarity is a broad concept and its value is normally obtained by averaging the opinion of several annotators.


For the RTE 1-4 corpora, we use the same SR interval scores and the same type of TE values, that means that a pair of sentences could have a SR score on a 5-point-semantic-scale [1-5] that ranges from 1 (completely unrelated) to 5 (very related) and that there are TE values ENTAILMENT, CON-TRADICTION and NEUTRAL.

as the texts in RTE corpora are usually not equal in size/length between the text (T) and the hypothesis (H), and most of the time, one text is (much) longer than the other one, we need to have a specific rule for this case.


Few cases were discussed, less than 5%, in order to leverage over big differences in annotation.

the annotation difficulty was not homogeneous among the RTE 1-4 corpora.


The RTE-4 introduces many to one mapping.

it is not clear how this could influence the performances of certain systems overall.


We present the plot for each corpora separately (in the final version of the paper).

only the degree of this correlation may vary, but it is general that ENTAILMENT is associated with high similarity.


A different module carries out a deep analysis on the sentence structure in order to decide the entailment especially for those border line cases signaled by the SR score, that is scores that are not very high but not very low either The entailment judgment is used to recompute the SR score, according to the following rule described in Figure 9.

the Structural Module in Figure 11 should employ a different class of techniques for determining the entailment.


It is desirable then to be able to automatically extract this information from users' content.

to the best of our knowledge there is no such resource for author profiling of health forum data.


Ideally, we should get recordings from the same bilingual or multilingual speaker for all the languages that are being mixed and then switch between databases while synthesizing the different languages.

getting such data and maintaining recording conditions across all the TTS databases may be difficult.


We conducted subjective tests comparing our manually mapped phonemes to the automatically mapped phonemes.

we found that in subjective tests, listeners had a very significant preference for the manually mapped phonemes.


Participants with high levels of extraversion were more influenced by the agent condition than participants with low extraversion.

as visible in Figure 4, these high extraverts diverged from the pattern of expressive behavior of the agents, making gestures with larger spatial extent when the agent moved to a more introverted style of nonverbal expressive behavior over the course of the interaction.


Distinguishing between the useful answer and the gibberish ones automatically may be extremely difficult, or even impossible.

during the following evaluation phase filtering out workers who don't Figure 1: Workflow of our approach for the generation of a Japanese ontology lexicon provide acceptable evaluations of the translations automatically would be much easier and could e.g.


On the one hand, that approach would probably be very expensive, and one may assume that crowdsourcing the task will be considerably cheaper.

with input from only one person, the variance of the received verbalizations would probably be lower than if possibly a lot of different people provide translations.


This time, results were significantly better, and there were no obvious word-by-word translations or workers who entered gibberish.

at around one month this run also took much longer to finish.


On the one hand, CrowdFlower uses these questions to test workers before the actual task starts in so-called quiz mode, where workers need to answer a certain amount of test questionsfive in our case -before they can actually work on the task.

also during the task itself a certain amount of the microtasks shown to the workers -in our setup twenty percent -are actually test questions.


For example, for the property yearOfConstruction the Japanese gold standard, among other entries, contains the construction (に)完成する, which is a rather general term that could be translated as to complete [in].

the English gold standard we worked with contained the more specific constructed [in] as the only verbalization of yearOfConstruction.


In general, different people tend to describe situations subjectively, with a varying degree of detail.

texts from the TAKING A BATH and PLANTING A TREE scenarios contain a relatively smaller number of sentences and fewer word types and tokens.


More than 2 out of 3 participants in total belong to one of only 5 labels.

the distribution for events is more balanced.


On the one hand, the manual creation of widecoverage knowledge bases is infeasible, due to the size and complexity of relevant script knowledge.

texts typically refer only to certain steps in a script and leave a large part of this knowledge implicit, relying on the reader's ability to infer the full script in detail.


In Aroyo and Welty (2012) the focus of crowdsourcing is not on assessing the ability of the crowd to perform a specific task, i.e., event detection, but on disagreement as a "natural state" suggesting that event semantics are imprecise and varied.

sprugnoli and Lenci (2014) evaluated the ability of the crowd in detecting event nominals in Italian, pointing out the complexity of this task due to the presence of ambiguous patterns of polysemy.


A less explored feature of the collection is the inclusion of alternative translations, which can be very useful for training paraphrase systems or collecting multi-reference test suites for machine translation.

differences in translation may also be due to misspellings, incomplete or corrupt data files, or wrongly aligned subtitles.


In most cases, they contain exactly one version per language, referring to either the source text or its translation.

there are cases in which one would like to consider alternative translations, for example, when evaluating machine translation using metrics such as BLEU (Papineni et al., 2002).


Certainly, these differences are not very interesting when looking for truly alternative translations.

misspellings are important to identify for further cleaning of the data or for filtering out corrupted portions of the collection.


However her heart swayed , Edith suffered .


Thus, WA research would highly benefit from gold standard data specifically tailored to assess WA systems on this is-sue.

to our knowledge, none of the available WA benchmarks specifically focuses on the problem of out-ofvocabulary (OOV) and rare words.


"voi") is missing, so in principle "you" could be left unaligned.

since the information about grammatical person is present in the verb, "you" was aligned with a P-link to "domanderete".


The number of these links is 14,598, out of which 84% are S-links (12,218).

since in this work we restrict the evaluation to only OOV/rare words, both the detailed statistics presented in Table 3 and the evaluation results given in Tables 8 and 9 refer to all and only the alignment links directly involving OOV/rare words.


The pioneer metric BLEU (Papineni et al., 2002) was originally designed to work with four reference sentences.

obtaining reference sentences is labour intensive 1 and expensive.


Meteor (Denkowski and Lavie, 2014), TERp (Snover et al., 2009) or ParaEval (Zhou et al., 2006).

out of these metrics, only Meteor is available for MT evaluation of Czech; we use it for comparison with our results.


The stochastic parse ranking module, which is part of the XLE distribution (Riezler and Vasserman, 2004), was trained on 34,000 manually disambiguated sentences (330,000 words).

to previous work on parse ranking for LFG treebanks (Cahill et al., 2007a), we are using discriminants as features in the log-linear model of the module.


This is the same idea as the one pursued by (Van der Wouden et al., 2015), who describe a project adding intelligent links to a grammatical database in the form of annotated queries to various Dutch language resources, thereby both making the query facilities more accessible and adding to the value of the grammatical database.

norsk referansegrammatikk is only available on paper, so in our case it is a question of structuring the documentation according to the chapter structure of the published grammar.


Expected results are observed using syntactic features that improve over a baseline as it was already demonstrated for DM (Ribeyre et al., 2015).

it is important to understand what is indeed improved with those features.


We exclusively identifies 356 constructs that are mostly "verbal masdar" ICs.


An interesting result of their study is that noun phrases in English informational writing have become increasingly elaborate over the last two centuries, making academic discourse a "compressed" genre.

biber and Gray (2011) do not relate this change to terminology, i.e.


1) lists key benefits for microservice-based architectures, including: "scaling": As DEREKO grows, the capacity of KorAP has to enhance as well.

scalability requirements differ depending on the component.


This indicates that blanket is a highly imageable word and would have a higher rating on an imageability scale.

the word honour does not form a mental image as easy as blanket, meaning that honour would have a lower value on the scale.


the nouns above the 50% line from the BOUN>CLC words did not get high AoA ratings, as indicated by the negative correlation between them (r (48) = -0.44, p<0.01).


When only considering German documents from the crawled data, the focused crawl yields consistently lower perplexity values and the difference increases as the crawl progresses.

while more data is collected, the larger becomes the fractional amount of relevant / German vs. irrelevant / non-German data.


Recently, statistical machine translation was also used for diacritic restoration of Hungarian (Novák and Siklósi, 2015).

we consider the method to be too complex for the task at hand as diacritic restoration does not require calculating word or phrase alignments, does not use phrases in the translation model and does not need to perform reordering.


Best results are obtained when taking into account both the probability of a form given its dediacritised version and the probability of the form in the given context (TM+LM).

very good results can be obtained already when using just the probability of each form given its dediacritised version (lexicon).


Tables 1 and 2, the symbol * represents a space 'inside' a word spelling, the last character of a word spelling being usually a space.

since word spellings are not necessarily 1-grams, not all word spellings recognised by our parser end in a space character.


The WMT15 QE shared task also followed this approach, using METEOR (Banerjee and Lavie, 2005) for a paragraph-level QE task (Bojar et al., 2015).


When these words were included among one of the unrelated alternatives, they were replaced by other candidates following the same criteria for frequency and number of senses as before.

when they were either among the targets or related words, they were simply removed from the resource.


Marmot contains utilities targeted at quality estimation at the word and phrase level.

due to its flexibility and modularity, it can also be extended to work at the sentence level.


These tasks might appear identical to the web search problem.

there is a number of distinct characteristics.


Multiple rank-ordering evaluation metric algorithms exist in the field of information retrieval (IR).

none of them is appropriate for the task described in the previous chapter.


The cost function was intentionally designed this way to bring more relevant search results closely to the top.

the rank-ordering problem needs a relative function with respect to the rest of the elements.


For example a list [9,1,1] will have different costs for [1,9,1] and [1,1,9].

we contend that the two lists are equally wrong because the algorithm decided that element of rank 9 is rank 1.


This problem is very challenging and the results are far from perfect.

to demonstrate shortcomings of popular rank-measures we create four tests: 1) we limit the data and produce a bad ranking prediction using limited part-of-speech analysis, 2) a slightly better rank predictions using LIWC word list (Pennebaker et al., 2001), 3) further improved ranker using n-gram approach, and 4) the perfect prediction, comparing the reference with itself.


TextRank outperforms in all preprocessing settings.

no algorithm outperform the PS-baseline.


It suggests that proper word segmentation has the positive effect in experiment 1 with an average score 0.07.

proper word segmentation has a negligible effect for Experiment 2 and 3.


From these results, it may be concluded that proper word segmentation improves the summarization results marginally.

it is worth note that the resources (stopwords lists, lemmatizer, and stemmer) are built on space segmented words.


The possible reason for the similar results may be that, probably, both have the same level of inconsistent over-stemming, causing data sparseness problem.

fix 1 seem to have consistent over-stemming, which reduces sparseness.


Although we consciously exclude relations between entities expressed as pronouns, this does not seem to result in significant loss of information, as abstracts and introductions occur early in the paper and usually contain the first mention of an entity.

other limitations stem from entity annotation errors, in particular bad delimitation.


When processing news (and news-style) documents, SUTime and HeidelTime perform similarly.

two major differences between them are that HeidelTime is multilingual, and that it applies different normalization strategies depending on the domain of the documents that are to be processed.


Explicit expressions can be directly normalized with standard temporal knowledge (e.g., 2016-01 for January 2016) and implicit expressions with non-standard temporal knowledge such as information about holidays (e.g., 2016-03-17 for Saint Patrick's Day 2016).

the normalization of relative and underspecified expressions requires a reference time for the normalization, and a relation to the reference time for underspecified expressions, additionally.


Note that the WikiWars corpus contains annotations in TIMEX2 format and that both HeidelTime and SUTime extract temporal expressions following TimeML's TIMEX3 tags.

a meaningful comparison is nevertheless possible as (i) many TIMEX values are identical in TIMEX2 and TIMEX3, (ii) some simple mappings from TIMEX3 to TIMEX2 are possible and we applied them for SUTime's and HeidelTime's output, and (iii) the other differences affect HeidelTime's and SUTime's evaluation results in the same manner.


A further temporal tagger distinguishing between newsand narrative-style documents is DANTE (Mazur and Dale, 2009).

dANTE extracts temporal expressions following the older TIdES TIMEX2 annotation guidelines, and modern efforts and evaluation tasks have focused on the different TIMEX3 standard for many years.


Examples are chronic, as in chronic depression or treatment-resistant, as in treatment-resistant anxiety disorder.

in the context of disorder, we decided to exclude a set of variants.


In addition, both the virtue and vice aspects of these foundations are more often invoked.

conservatives score higher in the loyalty and authority moral foundations, similar to theory.


Using an automated approach has the advantage of gathering data quickly and with fewer resources.

as with any other method, validation of the method is required.


Table 11: Evaluation results of various n-gram models for the second step of two-step classification framework: gold standard test set experiments It is evident from the results obtained through different experimental setups that temporal classifier, in general, performs remarkably well while we deal only with two classes, namely temporal and atemporal.

results are not up to the mark while we attempt to perform classification with all the five classes (four temporal and one atemporal classes).


al, 2012;Morante & Blanco, 2012;Vincze et al, 2008;Pyysalo et al., 2007).

in conversational texts, the scope and focus of negation may be located in the same utterance or in the previous dialogue context (i.e., inter-sentential negation), such as the previous utterance.


Natural language question answering (QA) provides an intuitive interface for retrieving EHR data by reducing the need to understand the internal organization of the data.

since this data is stored in both unstructured text and structured databases, a deep semantic understanding of EHR questions is necessary for an effective QA system.


In this work, our goal is to provide a sufficient number of question/logical form pairs to train a baseline semantic parser.

the broad range of the medical domain likely means that additional types of data will be necessary to achieve human-like semantic parsing capabilities for EHR questions.


Other common predicates were quite high, such as δ (0.95), the has * predicates (0.96), and time within (0.95).

agreement on the latest predicate was less impressive (0.64).


For example, one annotator marked just in just minutes apart as a MODIFIER(TYPE=APPROX).

since just minutes represents the same period as minutes does (i.e., just minutes is not an approximate version of minutes) just should not have been annotated as a MODIFIER.


Other projects aim at annotating discourse relations between clauses, among which causal relations, marked or not by discourse connectives such as because or then (Prasad et al., 2008;Carlson et al., 2007).

larger lexical projects have on the one hand covered all sorts of POS expressing causality (including nouns, verbs, adverbs, conjunctions, prepositions, adjectives) and on the other hand, distinguished a much larger set of causal relationships involving events as well as facts: for instance in FrameNet (henceforth FN) (Baker et al., 1998), some frames are concerned with argumentation, where typical causal expressions introduce evidence for a claim, or reasons for an agent's behaviour.


These systems extract semantic links between verbs and their arguments.

the work presented here complements semantic role representations with temporally-anchored spatial knowledge.


According to the syntactic approach of PerDT, some word sequences like ""(promise + to give) to promise and ""(allow + to give) to allow have been considered to have the above-mentioned structure in which the words "" and "" are the objects of the simple verb "" not the non-verbal elements of the complex predicate ""or ""; more clearly the above word sequences are not considered as complex predicates and considered just as a combination of a noun (with the syntactic role object) and a simple verb.

since in PerPB every event or action with inflectional properties of the verbs in the Persian is treated as the verb, the previously mentioned sequences are considered individual verbs.


ACL is also characterized by a larger proportion of data (DATA-ITEM) being discussed than in ACM, and, as expected, natural languages (LANGUAGE) appear more frequently in ACL.

the items labeled INTELLIGENT-AGENT characterize the ACM set, owing to several articles about electronic commerce.


Accordingly, the past tense is assumed to be used to describe events that are located before the moment of utterance.

since Reichenbach (1947) at the latest, it has been known that tense and aspect cannot be adequately analysed without taking into account a third component, labeled 'Reference Time' by Reichenbach (1947), and explicated by Klein (1994) as 'Topic Time'.


1 Ideally new PNs extracted from collections of diachronic text news are OOV PNs with respect to the LVCSR vocabulary.

all new PNs are not present in the test set audio documents.


Related works have been done using syllable-based acoustic modeling on large-vocabulary continuous speech recognition (LVCSR) for both monosyllabic and polysyllabic languages, including Mandarin (Lee et al., 1993;Pan et al., 2012;Deng Li and Li Xiao, 2013;Hu et al., 2014;X. Li, and X. Wu, 2014) and West languages (Hinton et al., 2012;Swietojanski et al., 2013;Gupta & Boulianne, 2013;Schmidhuber 2015).

automatic STT on Cantonese is far behind.


the official spelling is based on the Latin alphabet called Boko.


H, L, LL, HH or E as defined in (Brognaux et al., 2013)).

we defined a specific type of accentual phrase (which could be considered as closer to intonational phrases) that only considers groups of words ending in higher level boundaries (i.e.


The original motivation for the SPA platform was to answer the increasing number of requests received for transcribing audio/video files in European Portuguese.

it was firstly created to provide a simple interface, easy to use by non-expert users, but requests have multiplied and diversified, namely in terms of languages (Spanish, English) and varieties covered (European, Brazilian), domains (broadcast news, interviews), etc.. although the majority of the SPA users are only interested in the automatic transcripts, other technology partners showed interest in obtaining information about characteristics of the speakers (e.g.


The default models have been optimized for broadcast news captioning in several languages (Portuguese, English, Spanish).

recognition models trained with telephone speech were also recently made available in the context of SpeDial project.


Different classification methods from the Weka toolkit (Hall et al., 2009) have been applied, including: Naïve Bayes, Logistic Regression, Decision trees, Classification and Regression trees, and Support Vector Machines (SVM).

the best performance was achieved with SVM, which has been setup to use Sequential Minimal Optimization with a Linear kernel as the training algorithm.


Furthermore, speaker pairing and topic attribution were constrained so that no two speakers would be paired with each other more than once and no one spoke more than once on a given topic.

only a subset of 1155 manual transcriptions (annotated with disfluency, abandonment, and interruption information), contain-ing 223606 utterances, was annotated for dialog acts, using the SWBD-DAMSL tag set (Jurafsky et al., 1997).


in the US over 200 million citizens are eligible to vote and thus can participate in RegulationRoom (Farina and Newhart, 2013;Park et al., 2012)).

the volume of data makes the task of its interpretation and summarisation extremely challenging.


As tweet-level geotagging remains rare, most prior work exploited tweet content, timezone and network information to inform geolocation, or else relied on off-the-shelf tools to geolocate users from location information in their user profiles.

such user location metadata is not consistently structured, causing such tools to fail regularly, especially if a string contains multiple locations, or if locations are very fine-grained.


Many existing geography-based Twitter visualisations are therefore limited to this highly biased subset of data.

tweet location is not a good proxy for a person's home location and can significantly distort the results of any study or visualisation which tries to capture information for different countries or regions.


The official Twitter documentation states that this record field is "[n]ot necessarily a location nor parseable".

we showed that the Edinburgh Geoparser can be adapted to carry out effective geolocation on user profiles.


Often, the proportion of tweets in favor of a target may not be similarly numerous as those against it.

we did not want scenarios where there are no tweets in favor of a target or no tweets against it.


It is clear that this method emphasized precision over recall, as it emulates a grep-style filtering program.

it does set a more challenging baseline than random assignment.


The tweet length is suitable for typical crowdsourcing tasks, thus, there is no need for pre-processing of T weet2014DS1 and T weet2015DS2 datasets.

to optimize the length of the news articles for the crowdsourcing task, each news article was split into text snippets, i.e., sentences.


The fact that about 84% of the total amount of tweets are considered relevant, out of which about 90% could indicate highly relevant tweets, shows that retrieving tweets based on relevant keywords or domain experts' seed words returns acceptable results.

there is still room for improvement, i.e., assessing the relevance of the tweets with regard to the event "whaling" is still necessary.


By performing this comparison we can state that usually, non-expert people have different ways to express or refer to a given event, in contrast to domain experts that have very specific terms to compose the space of an event.

this difference does not prove that the tweets can not contain useful information, e.g., T W EET 2 in Example 2, but it gives meaningful insights that the topic space given by the experts can be further enriched.


As a general trend, we observed that overlapping text snippets usually have higher similarity scores, while the non-overlapping text snippets have lower scores.

the relevance scores provided by the automated method have a much smaller interval, between 0 and 0.66.


used in similar context even if not together.

all the models are closest to the average scores, suggesting that the models learn a sort of combination of Similarity and Relatedness.


The proposed method was evaluated on a semantic similarity task (WS353 dataset), where the multimodal model was found to yield higher performance compared to the textual one.

the best performance was moderate (0.32 correlation coefficient).


(2011) trained multiple classifiers to handle coreference between event mentions of different syntactic types (e.g., verb-noun coreference, noun-noun coreference) on the OntoNotes corpus.

since event coreference links and entity coreference links are not distinguished in OntoNotes, Chen et al.


For the command recognition task, an equally-likely finite state grammar formed by all the unique possible command sentences was initially used.

in practice it was necessary to use an extended command grammar incorporating the background model to better handle inaccurate segmentations provided by the automatic SAD.


A considerable contribution to the recall decrease is due to the miss segmentation errors introduced by the SAD module (10.7%).

the inserted segmentation errors in combination with the challenging characteristics of the data contribute to the precision performance drop.


When using automatic segmentation, the command recognition performance was also remarkable in most of the acoustic conditions.

it dramatically degraded in the presence of overlapping and continuous voice-like noise, such as TV and radio.


For the emotion dimension estimation, the automatic crosscorpus emotion labeling was effective for some dimensions, showing only slight performance degradation.

we could not obtain sufficient performance for the emotion category estimation.


Speech-enabled interfaces have the potential to become one of the most efficient and ergonomic environments for human-computer interaction and for text production.

not much research has been carried out to investigate in detail the processes and strategies involved in the different modes of text production.


P19), and show much worse performance than in the translation mode.

it seems that the more time a translator needs for translation, the more likely he or she will be quicker with post-editing and dictation.


This might also explain an observation of Ciobanu (2014), who reports that "less experienced translators tend to stay away from ASR at the beginning of their careers", while "within the professional experience groups … ASR does have a positive impact on productivity": Translation students struggle often more with source text comprehension than expert translators, which may make it more difficult for them to overcome a word-by-word translation mode and to produce longer target text sequences, which are at the same time also crucial to reduce the ASR error rate and to enhance word recognition.

as outlined above, a number of parameters may play a role in a better acceptance and usage of the ASR technology in the translation community.


This route can complement existing tools for harvesting IGT from PDF documents, in particular ODIN (Lewis and Xia, 2010), which have to cope with much more noise in the source channel resulting from lossy pdf-to-text conversion.

parsing L A T E X comes with its own challenges, as detailed in section 5.


As there is no widely accepted orthographic standard for Swiss German, we use a set of recommendations proposed by Dieth (1986).

the implementation of the recommendations changed over the time.


The decrease in accuracy observed with Test1 was expected, since this document comes from a dialect that has not been seen in the training, which also shows in the higher proportion of unknown (New) words in this text.

automatic normalisation showed even better accuracy for Test2 than for crossvalidation; this text was dialectologically more closely related to the ones used for training the tool.


Data inputs are referred to here as examples, and the desired output values as labels.

learning algorithms require examples in specific formats.


If this feature extractor were instead called with the word constituent for "Jane" as its argument, it will extract the same features because its left context is the same.

a feature extractor that used the right context of the focus would produce different results, since the NER constituent covers an additional word, and the right context for that constituent will begin after the second word.


Up to our knowledge, we are the first to provide the readability annotation feature as a collaborative online tool to help in constructing training corpora for different readability services.

mADAD can also be used as a general-purpose annotation tool for Arabic text.


A way to understand the model by Louis and Nenkova (2012) is to see it as an alignment model between syntactic items.

that model does not have any latent variables, which is possible under the assumption that all available alignment configurations have been directly observed in the training data.


A limitation of the previous guidelines and annotation was the provision for a single CoreSC concept per sentence.

it may be the case that more than one CoreSC concept (e.g.


The smallest unit to be annotated are still sentences, even though in principle individual clauses may fit better with CoreSCs and reduce the overlap in annotations.

we decided against the annotation of clauses, as recently employed clause recognition algorithms seem to be purpose-built for specific application areas (e.g (Del Corro and Gemulla, 2013) or (Kanayama and Nasukawa, 2012)) and prior built clause detection mechanisms performed with Fmeasures up to 78.63% (Tjong et al., 2001), which in itself could introduce noise to the task of automatically identifying CoreSC concepts.


It means, however, that if there is disagreement on the first label potential agreements on the second and third labels do not count.

if two annotators agree on the first label but not on the rest, this can return a higher value than weighted kappa.


Table 3 shows that the annotators tend to disagree on when multiple annotations need to be assigned to a sentence which is why the resulting consensus largely (96%) consists of sentences with single annotations.

these single annotations per sentence may be more accurate overall as opposed to the previously published corpus as the final decision on the gold standard annotation has been derived from a conservative consensus, taking the priority of concepts and the most reliable annotator into account.


Table 4 shows that distributions of the different CoreSC categories are more or less the harmonised values from the different annotators.

the Model category ('Mod') stands out from the others in that it almost disappears from the gold standard.


The 8 features we used might not be as effective in capturing their distinct qualities.

it could also be that the content of satirical and non-satirical entertainment articles does not differ as much as is the case for the other topics, making it harder for the classifiers to differ between both classes.


WikiExtractor is designed to extract the text content, or other kinds information from the Wikipedia articles, while Sweble is a real parser that produces abstract syntactic trees out of the articles.

both WikiExtractor and Sweble are either limited or complex as users must adapt the output to the type of information they want to extract.


A similar German database is OpenThesaurus 4 , which is available under the GNU license.

it only provides synonym and association relations (Naber, 2004).


CL-ESA seems to show better results on comparable corpora, like Wikipedia.

cL-ASA obtains better results on parallel corpora such as JRc, Europarl or APR collections.


Statistical Machine Translation (SMT) relies on the availability of rich parallel corpora.

in the case of under-resourced languages or some specific domains, parallel corpora are not readily available.


The amount and diverse studies and efforts show that 1 http://www.accurat-project.eu/ 2 http://www.taas-project.eu/ 3 https://comparable.limsi.fr/ comparable corpora is certainly a useful resource to determine helpful material for SMT but also for related studies such as bilingual term extraction, cross-lingual information retrieval, etc.

most efforts have been related to European languages and less in middleeast languages.


There have also been a few attempts at implementing tools to aid in correspondence discovery (Lowe and Mazaudon, 1994;Covington, 1996).

these tools only assist in finding examples for hypothesized correspondences and do not allow to easily find completely new ones.


Usage in our scenario: It is easy to see that two-part MDL naturally guards against overfitting by weighing each rule's complexity against its utility.

if the desired correspondences are not purely statistical in nature, as is the case here, then we may benefit from abusing the MDL formalism slightly.


We check for applicability in the most general way possible: by simply seeing whether the strings associated by a rule are present in any of the word pairs.

many of the applicable-but-not-found rules correspond to quite deep linguistic processes.


in бос -босс (bos -boss) 'boss' or дискусия -дискуссия (diskusija -diskussija) 'discussion', found in iteration 141).

the model did not recognize the following regular orthographic correspondences between Bulgarian and Russian (Gribble, 1987;Valgina et al., 2002;Ivanova et al., 2011): (л:лл) [l:ll] (алигатор -аллигатор (aligator -alligator) 'alligator', колега -коллега (kolegakollega) 'colleague'); (р:рр) [r:rr] (перон -перрон (peron -perron) 'platform' etc.


Our diachronically-based orthographic correspondences already include both mentioned correlates.

in the Pan-Slavic word list, there are long forms of adjectives as cognates for Russian: BG зъл (zăl) -RU злой (zloj) 'wicked'.


Among the BG-RU correspondences that the model suggested are the following correspondences of noun endings, sorted by frequency upon discovery 7 (iteration in brackets): e.g., (а#:а#) 149 (12) (for feminine); (я#:я#) 40 (38) (for feminine); (о#:о#) 36 (45) (for neuter) etc.

the last ending is ambiguous and may also be an adverb ending, for example, BG-RU:много -много (mnogo -mnogo) 'a lot of'.


This architecture enables multiple annotators to work on different tasks simultaneously.

the administrator manages only one central database.


The [Begin, End] time interval of the French translation fragments will allow the alignment tool to identify the right fragment (the one that includes the [Begin, End] time interval of the sign).

since the very same translation fragment may be linked to several and possibly numerous successive LSFB signs, the tool will then need to further slice the translation fragment in smaller fragments, each relating to one "clause-like" unit in LSFB.


The alignments order was shuffled and this enabled the corpus to be available under CC-BY license through META-SHARE.

this prevents the research in language units over the sentence level.


Additionally, unexposed broadcast data from prior LDC collection efforts contributed additional recordings for Indian English, Mandarin, Modern Standard Arabic and US English.

to CTS, identification of individual speakers in the broadcast data is unfeasible.


We present FlexTag, a highly flexible PoS tagging framework.

to monolithic implementations that can only be retrained but not adapted otherwise, FlexTag enables users to modify the feature space and the classification algorithm.


Training FlexTag Most other trainable taggers only support one input format and users are supposed to transform their data in the required format.

flexTag makes no assumptions about the input format and relies on the UIMA reader concept supporting all readers that are compatible with the DKPro type system.


In this case, we simply request the text of the current token and check whether it starts with an @ sign.

more complicated actions like accessing neighbouring tokens are easily possible.